U.S. patent application number 13/029504 was filed with the patent office on 2011-09-22 for automated legal evaluation using bayesian network over a communications network.
Invention is credited to Harvey L. Gansner.
Application Number | 20110231346 13/029504 |
Document ID | / |
Family ID | 44648013 |
Filed Date | 2011-09-22 |
United States Patent
Application |
20110231346 |
Kind Code |
A1 |
Gansner; Harvey L. |
September 22, 2011 |
AUTOMATED LEGAL EVALUATION USING BAYESIAN NETWORK OVER A
COMMUNICATIONS NETWORK
Abstract
A method for legal knowledge modeling and automated legal
evaluation, such as for online, questionnaire-based legal analysis,
is provided. Information, such as facts and characteristics of a
legal situation or legal scenario, as it relates to a legal
conclusion or a legal result, in addition to the probabilities of
such conclusions or results, are modeled in a Bayesian Network. The
Bayesian Network may comprise instantiable nodes, fault nodes,
intermediary nodes, a utility node and decision nodes. The Bayesian
network is automatically updated on a periodic basis to reflect new
legislation or court decisions. Using Bayesian inference, the
conditional probability of a legal conclusion based on a user's
answers to a questionnaire may be determined. These conditional
probabilities are modified upon the input of evidence, which is
typically in the form of answers to a dynamic set of questions
designed to identify a legal conclusion or a legal result.
Inventors: |
Gansner; Harvey L.;
(Smithers, CA) |
Family ID: |
44648013 |
Appl. No.: |
13/029504 |
Filed: |
February 17, 2011 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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61340312 |
Mar 16, 2010 |
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Current U.S.
Class: |
706/11 ;
706/14 |
Current CPC
Class: |
G06Q 10/06 20130101;
G06N 7/005 20130101; G06Q 10/10 20130101; G06Q 50/18 20130101 |
Class at
Publication: |
706/11 ;
706/14 |
International
Class: |
G06N 5/02 20060101
G06N005/02; G06F 15/18 20060101 G06F015/18 |
Claims
1. A method on a computer for aiding in the analysis of a legal
matter using a Bayesian network, comprising: generating a Bayesian
network representing probabilistic relationships between a
plurality of legal inquiries and a plurality of legal conclusions,
wherein the Bayesian network comprises a plurality of nodes and a
plurality of edges connecting the nodes, wherein each node is
associated with a variable that represents either an answer to a
legal inquiry or a legal conclusion, and an edge represents a
conditional dependency between variables of nodes; associating a
probability function with each node, wherein a probability function
takes as input one or more values of variables from one or more
parent nodes and gives a probability of a child node's variable;
providing to a user, via a graphical user interface, the plurality
of legal inquiries; receiving from the user, via the graphical user
interface, answers to at least a portion of the legal inquiries;
replacing a variable of each node corresponding to an answer
provided by the user with a value representing the answer;
executing the probability function of each node, thereby
calculating the probability of each legal conclusion based on the
answers provided by the user; displaying for the user, via the
graphical user interface, the probability of each legal conclusion;
storing a record associated with the user, the probability of each
legal conclusion, as displayed for the user, the plurality of legal
inquiries and the plurality of legal conclusions; receiving a legal
update comprising a change in law that affects how the legal
conclusions are reached; modifying the Bayesian network in light of
the legal update; replacing a variable of each node in the modified
Bayesian network corresponding to an answer provided by the user
with a value representing the answer; executing the probability
function of each node in the modified Bayesian network, thereby
re-calculating the probability of each legal conclusion based on
the answers provided by the user; and wherein if the probability of
each legal conclusion in the modified Bayesian network does not
match the probability of each legal conclusion in the record that
was stored, sending a message to the user.
2. The method of claim 1, wherein the step of providing to a user
the plurality of legal inquiries comprises: providing to a remote
user, over a communication network via a graphical user interface,
the plurality of legal inquiries.
3. The method of claim 2, wherein the step of receiving from the
user answers to at least a portion of the legal inquiries:
comprises: receiving from the remote user, over a communication
network via the graphical user interface, answers to at least a
portion of the legal inquiries.
4. The method of claim 3, wherein the step of displaying for the
user the probability of each legal conclusion comprises: displaying
for the remote user, over a communication network via the graphical
user interface, the probability of each legal conclusion.
5. The method of claim 4, wherein the step of sending a message to
the user comprises: generating and sending an email to the user
specifying that probability of the legal conclusion previously
provided to the user has changed.
6. The method of claim 4, wherein the step of sending a message to
the user comprises: generating a sending a text message to the user
specifying that probability of the legal conclusion previously
provided to the user has changed.
7. The method of claim 1, wherein the step of receiving a legal
update comprises: receiving an automated legal update over a
communication network.
8. A method on a computer for generating and periodically updating
a Bayesian network used in aiding in the analysis of a legal
matter, comprising: generating a Bayesian network representing
probabilistic relationships between a plurality of legal inquiries
and a plurality of legal conclusions, wherein the Bayesian network
comprises a plurality of nodes and a plurality of edges connecting
the nodes, wherein each node is associated with a variable that
represents either an answer to a legal inquiry or a legal
conclusion, and an edge represents a conditional dependency between
variables of nodes; associating a probability function with each
node, wherein a probability function takes as input one or more
values of variables from one or more parent nodes and gives a
probability of a child node's variable; receiving a legal update
comprising a change in law that affects how the legal conclusions
are reached; automatically identifying how the Bayesian network
must be modified in light of the legal update; automatically
modifying the Bayesian network as identified in light of the legal
update; and periodically performing the receiving, identifying and
modifying steps.
9. The method of claim 8, wherein the step of automatically
identifying how the Bayesian network must be modified in light of
the legal update comprises one or more of: identifying which of the
nodes, edges, probability functions, plurality of legal inquiries
or plurality of legal conclusions should be deleted; identifying
how one or more nodes, edges, probability functions, plurality of
legal inquiries or plurality of legal conclusions should be
modified; and identifying which nodes, edges, probability
functions, legal inquiries or legal conclusions should be added to
the Bayesian network.
10. The method of claim 9, wherein the step of receiving a legal
update comprises: receiving an automated legal update over a
communication network.
11. A method on a computer for generating and periodically updating
a Bayesian network used in aiding in the analysis of a legal
matter, comprising: generating a Bayesian network representing
probabilistic relationships between a plurality of legal inquiries
and a plurality of legal conclusions, wherein the Bayesian network
comprises a plurality of nodes and a plurality of edges connecting
the nodes, wherein each node is associated with a variable that
represents either an answer to a legal inquiry or a legal
conclusion, and an edge represents a conditional dependency between
variables of nodes; associating a probability function with each
node, wherein a probability function takes as input one or more
values of variables from one or more parent nodes and gives a
probability of a child node's variable; receiving a legal update
comprising a change in law that affects how the legal conclusions
are reached; automatically identifying which legal inquiries or
legal conclusions must be modified in light of the legal update;
displaying for a user, via a graphical user interface, the legal
inquiries or legal conclusions that were identified to be modified;
receiving from the user, via the graphical user interface, a
description of modifications to the Bayesian network that must be
performed in light of the legal update; and modifying the Bayesian
network as described by the user.
12. The method of claim 11, wherein the step of receiving a legal
update comprises: receiving an automated legal update over a
communication network.
13. The method of claim 11, wherein the step of displaying for the
user the legal inquiries or legal conclusions comprises: displaying
for the remote user, over a communication network via a graphical
user interface, the legal inquiries or legal conclusions that were
identified to be modified.
14. The method of claim 11, wherein the step of receiving from the
user a description of modifications comprises: receiving from the
remote user, over a communication network via the graphical user
interface, a description of modifications to the Bayesian network
that must be performed in light of the legal update.
15. The method of claim 11, wherein the description of
modifications to the Bayesian network comprises one or more of: an
identification of which of the nodes, edges, probability functions,
plurality of legal inquiries or plurality of legal conclusions
should be deleted; an identification of how one or more nodes,
edges, probability functions, plurality of legal inquiries or
plurality of legal conclusions should be modified; and an
identification of which nodes, edges, probability functions, legal
inquiries or legal conclusions should be added to the Bayesian
network.
16. The method of claim 11, further comprising: replacing a
variable of each node in the modified Bayesian network
corresponding to an answer provided by the user with a value
representing an answer provided by a user; executing the
probability function of each node in the modified Bayesian network,
thereby calculating the probability of each legal conclusion based
on the answers provided by the user; and wherein if the probability
of each legal conclusion in the modified Bayesian network does not
match the probability of each legal conclusion in a previously
stored record associated with the user, sending a message to the
user.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This patent application claims priority to provisional
patent application No. 61/340,312 filed Mar. 16, 2010. The subject
matter of provisional patent application No. 61/340,312 is hereby
incorporated by reference in its entirety.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] Not Applicable.
INCORPORATION BY REFERENCE OF MATERIAL SUBMITTED ON A COMPACT
DISC
[0003] Not Applicable.
BACKGROUND OF THE INVENTION
[0004] 1. Field of the Invention
[0005] The present invention relates to the field of legal analysis
and, more specifically, the present invention relates to the field
of automated legal analysis over a communications network.
[0006] 2. Description of the Related Art
[0007] The evaluation of a legal case or a legal scenario can be a
complex undertaking. There are often a myriad of state and federal
laws and regulations, as well as judge-made law, which must be
taken into account in order to reach a thorough and complete legal
conclusion. Often, the facts surrounding the legal situation itself
can be a difficult to understand and categorize. A criminal case
involving forensic accounting, for example, may include the
consideration of thousands of individual facts. The complexity of
such an analysis is compounded by the fact that new legislation and
court decisions are issued every day that can have an effect on the
legal analysis being made. For example, the U.S. Board of Patent
Appeals and Interferences at the U.S. Patent and Trademark Office
issues ten to twenty decisions on any given work day, any of which
can make a difference in a legal analysis involving patent law. In
light of the above, it is no wonder that billions of dollars are
expended every year in the U.S. in the course of evaluating legal
situations.
[0008] Various approaches to the problems of complex legal analysis
have been disclosed. The conventional approach utilized by the
majority of the legal industry today involves the time-honored
routine of having an attorney or team of attorneys and other legal
professionals amass all relevant facts associated with a legal
scenario, conduct legal research into all relevant laws,
regulations and court decisions, and write legal memorandums to
explore each legal issue separately. After all of the facts have
been pored over and all applicable laws have been evaluated in
light of the facts, the attorney(s) generate a theory of the case,
which is typically asserted in a final legal memorandum. Other than
using computers to perform the tasks above, the aforementioned
conventional system of legal analysis has not changed in more than
a century. As a result, when a prospective client walks into a
modern lawyer's office and requests an evaluation of his case, it
can often take weeks or months and many thousands of dollars until
such an evaluation is complete.
[0009] One popular automated approach, often employed by vendors of
tools to legal providers, involves the use of an inference engine.
Modern inference engines in the legal industry typically involve
sets of if-then rules that are executed to reach a legal conclusion
or evaluation. The user of the inference engine begins by entering
the facts of the case, often as answers to questions posed to the
user, into a computer interface that reads the entries. Any if-then
statements that match the given facts are executed, the result of
which is a legal conclusion or final legal evaluation.
[0010] The approaches above, however, have their drawbacks. One
problem with the conventional approach is its limited usability by
a single user. Due to the sheer magnitude of laws, regulations and
facts surrounding certain complex cases, it is simply not possible
for a single attorney or other legal professional to absorb all of
the applicable data and make a sound legal conclusion. Thus, in
complex cases, teams of legal professionals must be employed to
accomplish the task. This can be extremely costly and time
intensive. Another problem with the conventional approach is user
error. Since humans cannot perform at 100% accuracy for extended
periods of time, there is the risk that the evaluation of hundreds
of laws, regulations and court decisions may include mistakes that
affect the accuracy of the final legal conclusion of the legal
professional. This is an unacceptable risk in cases where large
amounts of money or the freedom of the client is at stake. Finally,
legal professionals are subject to their own biases, due to the
party represented (plaintiff or defendant), gender, race, etc. This
can cloud a legal professional's judgment and affect the accuracy
of his or her legal evaluation.
[0011] One problem with both the conventional approach and the
inference engine approach is the lack of the ability to account for
probabilities in legal evaluations. An inference engine using
if-then statements, for example, reaches a hard and fast conclusion
or result. That is, the result may be a "yes" or "no." In the legal
world, however, guarantees of a win or loss in any given case are
rarely given, since the final decision maker is a person or
persons--i.e., a judge, board or jury--and decisions can vary
widely. For this reason, probabilities would be a more accurate
method for presenting a legal analysis. Further, the approaches
above do not provide a mechanism for showing or explaining the
relationships between the given facts of the case and the legal
conclusions or analysis. Thus, this limits the ability of the
aforementioned approaches to educate the user on how various
aspects of the legal scenario interact with each other. Lastly, the
approaches above do not adequately account for the fast paced
issuance of new laws, regulations and court decisions that could
affect the result of a legal evaluation. This brings into question
the validity of any legal conclusion reached by a system that does
not take the most recent laws into account.
[0012] Therefore, what is needed is a system and method for
improving the problems with the prior art, and more particularly
for a more efficient method and system for evaluating legal
situations and scenarios.
BRIEF SUMMARY OF THE INVENTION
[0013] Embodiments of the present invention address deficiencies of
the art in respect to automated legal analysis and provide a novel
and non-obvious method, computer and computer program product for
aiding in the analysis of a legal matter using a Bayesian network.
In an embodiment of the invention, the steps performed by the
method, server and computer program product of the present
invention include:
(a) generating a Bayesian network representing probabilistic
relationships between a plurality of legal inquiries and a
plurality of legal conclusions, wherein the Bayesian network
comprises a plurality of nodes and a plurality of edges connecting
the nodes, wherein each node is associated with a variable that
represents either an answer to a legal inquiry or a legal
conclusion, and an edge represents a conditional dependency between
variables of nodes; (b) associating a probability function with
each node, wherein a probability function takes as input one or
more values of variables from one or more parent nodes and gives a
probability of a child node's variable; (c) providing to a user,
via a graphical user interface, the plurality of legal inquiries;
(d) receiving from the user, via the graphical user interface,
answers to at least a portion of the legal inquiries; (e) replacing
a variable of each node corresponding to an answer provided by the
user with a value representing the answer; (f) executing the
probability function of each node, thereby calculating the
probability of each legal conclusion based on the answers provided
by the user; (g) displaying for the user, via the graphical user
interface, the probability of each legal conclusion; (h) storing a
record associated with the user, the probability of each legal
conclusion, as displayed for the user, the plurality of legal
inquiries and the plurality of legal conclusions; (i) receiving a
legal update comprising a change in law that affects how the legal
conclusions are reached; (j) modifying the Bayesian network in
light of the legal update; (k) replacing a variable of each node in
the modified Bayesian network corresponding to an answer provided
by the user with a value representing the answer; (l) executing the
probability function of each node in the modified Bayesian network,
thereby re-calculating the probability of each legal conclusion
based on the answers provided by the user; and (m) wherein if the
probability of each legal conclusion in the modified Bayesian
network does not match the probability of each legal conclusion in
the record that was stored, sending a message to the user.
[0014] Additional aspects of the invention will be set forth in
part in the description which follows, and in part will be obvious
from the description, or may be learned by practice of the
invention. The aspects of the invention will be realized and
attained by means of the elements and combinations particularly
pointed out in the appended claims. It is to be understood that
both the foregoing general description and the following detailed
description are exemplary and explanatory only and are not
restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0015] The accompanying drawings, which are incorporated in and
constitute part of this specification, illustrate embodiments of
the invention and together with the description, serve to explain
the principles of the invention. The embodiments illustrated herein
are presently preferred, it being understood, however, that the
invention is not limited to the precise arrangements and
instrumentalities shown, wherein:
[0016] FIG. 1 is a block diagram illustrating the network
architecture of a system for aiding in the analysis of a legal
matter using a Bayesian network over a communications network, in
accordance with one embodiment of the present invention.
[0017] FIG. 2 is a flow chart describing the control flow of the
process for setting up and updating the Bayesian network of FIG. 1
over a communications network, in accordance with one embodiment of
the present invention.
[0018] FIG. 3 is a flow chart describing the control flow of the
process for executing the Bayesian network of FIG. 1 over a
communications network, in accordance with one embodiment of the
present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0019] The present invention improves upon the problems with the
prior art by providing a more effective and efficient automated
method and system for providing quick and simple legal analysis
using probabilities of legal conclusions or results. The present
invention improves over the prior art by increasing usability by a
single user, even in complex legal cases that involve large amounts
of facts and numerous laws or regulations. This feature saves time
and expenses by providing an empirical data-based legal conclusion
in a short period of time. The present invention also improves upon
the prior art by providing higher accuracy in the legal evaluation.
Due to its automated nature, the present invention does not rely on
a human to make legal analysis decisions, thereby reducing or
eliminating the risk of making a mistake in the course of
evaluating large numbers of facts, laws, regulations and court
decisions. Further, the automated nature of the present invention
removes the natural bias of a legal professional in the legal
analysis process, thereby providing for a more accurate legal
conclusion.
[0020] The present invention further improves upon the conventional
approach and the inference engine approach by providing the ability
to account for probabilities in legal evaluations. This is
advantageous since probabilities are a more practical and
understandable method for presenting a legal analysis. Further, the
present invention provides a mechanism for showing or explaining
the relationships between the given facts of the case and the legal
conclusions or analysis, thereby providing an educational benefit
to the user. Lastly, the present invention adequately accounts for
the fast paced issuance of new laws, regulations and court
decisions that could affect the result of a legal evaluation. The
periodic update feature of the present invention provides an
automated mechanism for updating legal conclusions based on new
legal updates and even notifying the user if a legal conclusion has
changed in light of the legal update.
[0021] Referring now to the drawing figures in which like reference
designators refer to like elements, there is shown in FIG. 1 a
block diagram illustrating the network architecture of a system for
aiding in the analysis of a legal matter using a Bayesian network
over a communications network, in accordance with one embodiment of
the present invention. FIG. 1 shows an embodiment of the present
invention wherein users 110-112, each comprising an individual and
a computer, interact with server 102 over a network 106, which can
be a packet switched network such as the Internet or the World Wide
Web. The computer of users 110-112 can be a desktop, a laptop,
handheld computer, a smart phone, a tablet computer or the
like.
[0022] Server 102, which may be a web server, is the main operative
element of the present invention, executing the steps that comprise
the method of the present invention. Server 102 includes a software
engine that delivers applications and data content (including text
files, HTML files, music files, video files, electronic book files,
app files, information files, and any other media content) to users
110-112. Server 102 may also deliver data content to users 110-112
based on search parameters or identifying information selected by a
client. It should be noted that although FIG. 1 shows only two
users 110-112 and one server 102, the system of the present
invention supports any number of client users and web servers
connected via network 106.
[0023] Server 102 includes program logic 155 comprising computer
source code, scripting language code or interpreted language code
that is compiled to produce computer instructions that perform
various functions of the present invention. In one embodiment of
the present invention, the program logic is a scripting language.
Program logic 155 may reside on a client computer, the server 102
or any combination of the two.
[0024] FIG. 1 further shows that server 102 is connected to a user
record database 122 and a legal content database 126. Database 122
is used to store user records, such as profiles and other user
account data, which have been created for each user 110-112.
Database 126 stores all legal data content of the present
invention. Databases 122 and 126 are collectively referred to as
the "data repository" or the "central repository" for all resident
data served by server 102 in the present invention. Note that
although FIG. 1 shows only two databases 122 and 126, the present
invention supports any number of databases holding various types of
data that is served by server 102.
[0025] FIG. 1 also shows a payment authority 145 to effectuate
payments by users 110-112 for legal data content. In one embodiment
of the present invention, the payment authority 145 is a payment
gateway that authorizes payments and transfers funds from one
entity, the buyer, to another, the seller. Payment gateways accept
payment via the use of credit cards, charge cards, bank cards, gift
cards, account cards, etc.
[0026] FIG. 1 also shows a third party legal data content provider
148, which provides updates on legal data content. Lastly, FIG. 1
shows a backup server 140 which makes copies of data on server 102
and/or its associated databases 122 and 126, so that these
additional copies may be used to restore the original after a data
loss event. The backup server 140 may be used to restore a state
following a disaster or to restore small numbers of files after
they have been accidentally deleted or corrupted.
[0027] Note that although server 102 is shown as a single and
independent entity, in one embodiment of the present invention, the
functions of server 102 may be integrated with the functions of
another entity, such as entities 140, 145, and 148 of FIG. 1.
Further server 102 and its functionality, according to a preferred
embodiment of the present invention, can be realized in a
centralized fashion in one computer system, or in a distributed
fashion where different elements are spread across several
interconnected computer systems.
[0028] FIG. 2 is a flow chart describing the control flow of the
process for setting up and updating the Bayesian network of FIG. 1
over a communications network (i.e., network 106), in accordance
with one embodiment of the present invention. The flow chart of
FIG. 2 describes the process undertaken during the setup of the
Bayesian network by the administrator 112, as well as the periodic
updating of the network. The flow chart of FIG. 2 is described in
association with FIG. 1.
[0029] For exemplary purposes, a running example shall be used
involving a legal query into the constitutionality or propriety of
a criminal drug-related arrest of a legal client. In a first step
202, an administrator or legal professional 112 reads or otherwise
consults with the latest state of the law on a particular
subject--in this case, drug-related criminal arrests. The
administrator 112 may consult, for example, federal and state
statutes, regulations, rules, treaties, laws, court decisions,
administrative decisions, legal opinions from non-governmental
authorities and the like. This step may comprise downloading or
accessing legal information from a legal content provider 148 in
exchange for a fee using payment authority 145. The legal content
downloaded from 145 may be stored in database 126.
[0030] Based on the current state of the law on this subject, in
step 204 the administrator 112 generates and uploads a set of legal
conclusions to server 102. In this example, the legal conclusions
may include: a) the arrest violated the constitutional rights of
the client and b) the arrest did not violate the client's
constitutional rights, i.e., it is constitutional. In one
alternative to the upload of legal conclusions, the administrator
may upload other outcomes, such as guidance, possible courses of
action, possible legal consequences, relevant and applicable case
law, relevant and applicable statutes, samples of correspondence
that the user may need to complete in order to pursue a particular
course of action, and electronic hyperlinks to other relevant or
helpful websites of any kind. That is, the administrator may upload
data and educational information that is displayed for the user
after he has undergone the data input process of 302-306 below.
[0031] In step 206, the administrator 112 generates and uploads a
set of variables representing facts relevant to the legal
conclusions of step 204. In this example, the variables
representing facts may include: 1) whether the drug at issue was
regulated by federal law, 2) whether the law enforcement officer
had probable cause to arrest the client, and 3) whether the
requisite amount of the drug at issue was found on the client's
person.
[0032] In step 208, the administrator 112 generates and uploads a
description of dependencies between the facts of step 206 and
between facts and the conclusions of step 204. For example, the
administrator may specify that the conclusion of whether the arrest
violated the constitutional rights of the client is dependent on
the facts 1), 2) and 3) above--i.e., the question of whether the
arrest violated the constitutional rights of the client is
dependent on whether the drug was regulated by federal law, whether
the law enforcement officer had probable cause to arrest the client
and whether the requisite amount of the drug was found on the
client's person.
[0033] In step 210, the administrator 112 generates and uploads
probability functions for the conclusions of step 204. A
probability function takes as input one or more values of variables
from one or more facts on which the legal conclusion depends. Based
on the values of those variables, the probability function provides
a probability that the legal conclusion is true. For example, the
administrator may specify a probability function dictating that the
probability the arrest did not violate the constitutional rights of
the client is 70% if the facts showed only that the drug was
regulated by federal law and the law enforcement officer had
probable cause to arrest the client. The same probability function,
however, may dictate that the probability the arrest did not
violate the constitutional rights of the client is only 20% if the
facts showed only that the requisite amount of the drug was found
on the client's person.
[0034] The probability functions of step 210 may represent
empirical data garnered from legal authorities, such as courts of
law. In one embodiment of the present invention, the probability
functions of step 210 may represent the probabilities of certain
legal outcomes, as shown by empirical data pertaining to court
judgments, jury decisions, judge decisions, board decisions, etc.
that relate to the same legal issues and facts entered in steps
202-206. Further, the probability functions of step 210 may be
specific to certain judges or judge panels, such that the
probabilities of certain legal outcomes may be reviewed according
to the identity of the judge or judges presiding over a case. The
legal facts, conclusions, dependencies and probability functions of
steps 204-210 may be stored in database 126.
[0035] In one alternative to a probability function, in step 210,
the administrator provides other program logic for generating a
legal conclusion or decision based on one or more values of
variables from one or more facts. For example, the administrator
may input decision tree reasoning, simple predicate logic, or other
suitable AI techniques. A decision tree is a decision support tool
that uses a tree-like graph or model of decisions and their
possible consequences, including chance event outcomes, resource
costs, and utility. Predicate logic is the generic term for
symbolic formal systems like first-order logic, second-order logic,
many-sorted logic or infinitary logic.
[0036] In step 212, the program logic 155 of server 102 generates a
Bayesian network representing probabilistic relationships between
variables representing facts and a plurality of legal conclusions.
The Bayesian network of step 212 is based on the legal conclusions,
variables, dependencies, and probability functions defined in steps
204-210. Step 212 includes generating a node for each legal
conclusion of step 204 and a node for each set of variables
representing facts (of step 206) relevant to the legal conclusions.
Step 212 further includes inserting edges between nodes, wherein an
edge represents a conditional dependency between variables of
nodes, as those dependencies are defined in step 208. Lastly, the
probability functions defined in step 210 are entered in each node
representing a legal conclusion.
[0037] A Bayesian Network may comprise, and the present invention
may generate, via step 212, instantiable nodes, fault nodes,
intermediary nodes, a utility node and a decision node.
Instantiable nodes are nodes into which evidence is entered.
Usually, they will correspond to questions with discrete or
continuous input that are instantiated by the user; i.e., evidence
"observed" by the user will be entered to the network at these
nodes. Fault nodes are output nodes, the results of which are of
interest to the user. Decisions and the information sought to be
provided by the network are modeled in these nodes. These nodes are
not instantiated. They are monitored for answers that are needed.
In a network, there can be more than one fault node, and as a
result, fault nodes may be interpreted in conjunction or
separately. For example, one fault node could provide the legal
conclusion level, and another fault node could provide the overall
legal status of the client. Intermediary nodes are neither
instantiated nor monitored or faulted. Their purpose is grouping
and at times simplifying the overall network design. Utility nodes
provide the quantitative background for a decision node to make a
decision. The utility node comprises a table of values representing
utilities for various decisions given a state in the chief
complaint fault node. A decision node calculates a utility value
for all states in the decision node. To do this, the decision node
uses a table of corresponding utility values for all states in the
chief complaint fault node.
[0038] Decision nodes are fault nodes. The decision node will have
different decisions as its states. At any given time based on
probabilities for different states in the diagnosis fault node and
table of utilities in the utility node a utility value will be
calculated for all states of the decision node. Utilities are
provided such that the state with greatest utility will be
considered the best decision. Decisions could be of any nature.
Examples include a legal situation where the goal would be to find
out if the client's legal rights have been violated.
[0039] In step 213, a period of time passes. In step 214, a legal
update is received by server 102. A legal update may comprise new
legislation, laws or regulations or a new court or administrative
decision. In step 216 it is determined whether the Bayesian network
necessitates modification in light of the legal update. In one
embodiment, step 216 may be performed automatically by program
logic 155 and in another embodiment, step 216 may be performed with
the assistance of an administrator 112, wherein the administrator
reviews the legal update and provides instructs the server 102 as
to whether the Bayesian network must be defined. If the network
must be modified, control flows to step 218. Otherwise, control
flows back to step 213.
[0040] In step 218, it is determined how the Bayesian network must
be modified in light of the legal update. In one embodiment, step
218 may be performed automatically wherein the program logic 155:
a) automatically identifies which of the nodes, edges, probability
functions, facts or plurality of legal conclusions should be
deleted, b) automatically identifies how one or more nodes, edges,
probability functions, facts or plurality of legal conclusions
should be modified and c) automatically identifies which nodes,
edges, probability functions, facts or legal conclusions should be
added to the Bayesian network. In another embodiment, step 218 may
be performed with the assistance of an administrator 112, wherein
the administrator reviews the legal update and provides a
description to the server 102 of whether nodes, edges, probability
functions, facts or legal conclusions should be deleted, modified
or added.
[0041] In step 220, the Bayesian network is modified as defined in
step 218. In one embodiment, the modification is performed
automatically by program logic 155. In another embodiment, the
modification is performed with the assistance of administrator 112.
In this embodiment, the items identified in step 218 are presented
to the administrator via a graphical user interface so that the
administrator may review the items and decide how to modify the
Bayesian network. Subsequently, the administrator may manually
modify the nodes, edges, probability functions, facts or plurality
of legal conclusions of the Bayesian network. Control then flows
back to step 213.
[0042] FIG. 3 is a flow chart describing the control flow of the
process for executing the Bayesian network of FIG. 1 over a
communications network, in accordance with one embodiment of the
present invention. The flow chart of FIG. 3 describes the process
undertaken during the use of the Bayesian network by a user 110
over the network 106. The flow chart of FIG. 3 is described in
association with FIG. 1. The running example involving the legal
query into the constitutionality of a drug-related arrest of a
legal client will continue to be used herein.
[0043] In step 302, the user 110 accesses the web server 102 over
the network 106. In step 304, the user 110 identifies the legal
issue he would like to analyze. The user 110 may provide a search
parameter to server 102, which may respond with a list of legal
issues from which user 110 may select. For example, the user 110
may specify "drug-related arrests" as his legal topic of choice.
Based on this selection, the program logic 155 accesses the
Bayesian network corresponding to the legal issue identified by the
user 110. In step 306 the user 110 is presented with a graphical
user interface that displays a series of legal inquiries, each
corresponding to a legal fact of step 206. For example, the user
110 may be presented with questions such as: 1) Was the drug at
issue a Schedule 1 drug such as heroin? 2) Was the drug at issue
found by a police officer on your person? 3) How much of the drug
at issue was found on your person? Subsequently, the user 110
provides his answers to the legal inquiries via the graphical user
interface.
[0044] In step 308, program logic 155 executes the probability
functions of the Bayesian network based on the answers provided by
the user 110. That is, the variable of each node corresponding to
an answer provided by the user 110 is replaced with a value
representing the answer. Subsequently, the probability functions of
each node are executed, thereby calculating the probability of each
legal conclusion based on the answers provided by the user 110 in
step 306. In step 310, the graphical user interface displays the
probability of each legal conclusion to user 110.
[0045] In one alternative to the display of legal conclusions, in
step 310 the interface may display guidance, possible courses of
action, possible legal consequences, relevant and applicable case
law, relevant and applicable statutes, samples of correspondence
that the user may need to complete in order to pursue a particular
course of action, and electronic hyperlinks to other relevant or
helpful websites of any kind.
[0046] Also in step 310, a record associated with the user 110 is
stored in database 122. The record may also be associated with the
probability of each legal conclusion, as displayed for the user
110, the plurality of legal inquiries, the answers provided by the
user and the plurality of legal conclusions. In step 311, a period
of time passes. In step 312, a legal update is received. In step
314 (which may be executed in the same manner as step 216), it is
determined whether the legal update comprises a change in law
affecting how the legal conclusions are reached. If the legal
update comprises a change in law, then control flows to step 316.
Otherwise control flows back to step 311.
[0047] In step 316, the Bayesian network is modified in light of
the legal update. See steps 218-220 above for a description of how
the Bayesian network can be modified. In step 318, previous step
308 is re-executed. That is, the modified Bayesian network is
executed using the answers provided by the user 110, thereby
re-calculating the probability of each legal conclusion based on
the answers provided by the user. In step 320, it is determined
whether the probability of each legal conclusion in the modified
Bayesian network matches the probability of each legal conclusion
in the record that was stored in step 310. If there is a match,
then control flows back to step 311. If there is no match, then a
message, such as an email message, is sent to the user 110 in step
322, notifying him of the discrepancy.
[0048] In addition to the implementations described above with
relation to automated legal analysis, the present invention can
also be used in other decision-making capacities, such as medical
diagnosis, evaluation of infrastructures via an engineering
inspection, network health analysis and building code compliance.
In other embodiments, the present invention may be used as an
educational tool that highlights the most pertinent aspects of a
decision-making process.
[0049] The present invention can be realized in hardware, software,
or a combination of hardware and software in the system described
in the figures above. A system according to a preferred embodiment
of the present invention can be realized in a centralized fashion
in one computer system or in a distributed fashion where different
elements are spread across several interconnected computer systems.
Any kind of computer system--or other apparatus adapted for
carrying out the methods described herein--is suited. A typical
combination of hardware and software could be a general-purpose
computer system with a computer program that, when being loaded and
executed, controls the computer system such that it carries out the
methods described herein.
[0050] An embodiment of the present invention can also be embedded
in a computer program product, which comprises all the features
enabling the implementation of the methods described herein, and
which--when loaded in a computer system--is able to carry out these
methods. Computer program means or computer program as used in the
present invention indicates any expression, in any language, code
or notation, of a set of instructions intended to cause a system
having an information processing capability to perform a particular
function either directly or after either or both of the following:
a) conversion to another language, code or, notation; and b)
reproduction in a different material form.
[0051] A computer system may include, inter alia, one or more
computers and at least a computer readable medium, allowing a
computer system, to read data, instructions, messages or message
packets, and other computer readable information from the computer
readable medium. The computer readable medium may include
non-volatile memory, such as ROM, Flash memory, disk drive memory,
CD-ROM, and other permanent storage. Additionally, a computer
readable medium may include, for example, volatile storage such as
RAM, buffers, cache memory, and network circuits.
[0052] In this document, the terms "computer program medium,"
"computer usable medium," and "computer readable medium" are used
to generally refer to media such as main memory removable storage
drive, a hard disk installed in hard disk drive, and signals. These
computer program products are means for providing software to the
computer system. The computer readable medium allows the computer
system to read data, instructions, messages or message packets, and
other computer readable information from the computer readable
medium. The computer readable medium, for example, may include
non-volatile memory, such as Floppy, ROM, Flash memory, Disk drive
memory, CD-ROM, and other permanent storage. It is useful, for
example, for transporting information, such as data and computer
instructions, between computer systems.
[0053] Although specific embodiments of the invention have been
disclosed, those having ordinary skill in the art will understand
that changes can be made to the specific embodiments without
departing from the spirit and scope of the invention. The scope of
the invention is not to be restricted, therefore, to the specific
embodiments. Furthermore, it is intended that the appended claims
cover any and all such applications, modifications, and embodiments
within the scope of the present invention.
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